Bibliographic record
Abstract
When Iana Dogel came to Canada with her husband she was looking for a career change. She already had a PhD in Life Sciences, but decided to pursue an MBA from the UofA to gain career flexibility. Now, as a consultant with Grant Thorton, she uses her PhD/MBA expertise to access government incentives for tech companies. Previously she ran her own consulting and founded a med dev start-up that develops a solution for mental health. In this episode of WTJ?, Iana speaks about her experience of coming to Canada, the importance of immigrant voices, starting a company and how she networks as an introvert.WTJ is sponsored by TD Insurance.Got a question? Email us at: wtj@ualberta.caWhat the Job? is a University of Alberta Alumni Association podcastHosted by: Matt ReaProduced by: Jennifer Jenkins and Matt ReaMusic: Cottages by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 Licensecreativecommons.org/licenses/by/3.0/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".